Publication detail

Image Reconstruction in Electrical Impedance Tomography through 1D-Convolutional Neural Network

KOUAKOUO NOMVUSSI, S. MIKULKA, J.

Original Title

Type

article in a collection out of WoS and Scopus

Language

English

Original Abstract

This paper presents a comparative analysis of image reconstruction performance using a 1D-Convolutional Neural Network (1D-CNN) against the Total Variation algorithm and the Gauss-Newton algorithm. The evaluation, conducted across multiple tests conditions, demonstrates that the 1D-CNN consistently outperforms both conventional methods in terms of correlation coefficient and structural similarity index (SSIM). In noise-free scenarios, the 1D-CNN achieves significantly higher correlation and SSIM values, indicating superior reconstruction accuracy. Furthermore, in the presence of noise (30 dB and 60 dB), the performance of the Total Variation and Gauss-Newton algorithms deteriorates considerably, whereas the 1D-CNN maintains high correlation and SSIM values, demonstrating strong robustness to noise. These findings highlight the effectiveness of deep learning-based approaches for image reconstruction, making the 1D-CNN a promising alternative to traditional reconstruction techniques.

Keywords

1D- convolutional Neural Network, Total Variation, Newton-Gauss, EIT

Authors

KOUAKOUO NOMVUSSI, S.; MIKULKA, J.

Released

29. 4. 2025

Location

Brno

ISBN

978-80-214-6321-9

Book

Proceedings II of the 31st Conference STUDENT EEICT 2025

Pages count

BibTex

@inproceedings{BUT198079,
  author="Serge Ayme {Kouakouo Nomvussi} and Jan {Mikulka}",
  title="Image Reconstruction in Electrical Impedance Tomography through 1D-Convolutional Neural Network",
  booktitle="Proceedings II of the 31st Conference STUDENT EEICT 2025",
  year="2025",
  pages="5",
  address="Brno",
  isbn="978-80-214-6321-9"
}